Dataset Information

Sleep Disorders in Pediatric Migraine: A Questionnaire-Based Study.

ABSTRACT: There is a high comorbidity between migraine and sleep disorders (SD), with a mutual dependence between sleep and headache. This study aimed to analyze the relationship between headache features (migraine frequency and severity, migraine equivalents, use and efficacy of treatments) and sleep in pediatric migraine. Parents of children and adolescents with migraine completed the Children's Sleep Habits Questionnaire (CSHQ) and the Epworth Sleepiness Scale for Children and Adolescents (ESS-CHAD) and answered questions about headache characteristics. The presence of SD was defined according to CSHQ score. SD were detected in 72.9% of 140 subjects, but only 5.0% already received a diagnosis. Patients with SD presented statistically significant higher headache frequency (p = 0.031) and higher prevalence of migraine equivalents (p = 0.007). A higher CSHQ total score was associated with higher frequency of severe attacks (p = 0.012) and lower acute drug efficacy (p = 0.003). Significant positive correlations of sleep onset delay, sleep duration and nightwakings subscales with migraine frequency emerged. Our findings indicate that SD are highly prevalent in pediatric migraine and frequently associated with a higher headache severity and lower response to acute therapy, but often remain underdiagnosed. Improving sleep quality could help to reduce migraine intensity and disability and vice versa.

SUBMITTER: Voci A

PROVIDER: S-EPMC8396839 | biostudies-literature | 2021 Aug

REPOSITORIES: biostudies-literature

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Sleep Disorders in Pediatric Migraine: A Questionnaire-Based Study.

Voci Alessandra A Bruni Oliviero O Ferilli Michela Ada Noris MAN Papetti Laura L Tarantino Samuela S Ursitti Fabiana F Sforza Giorgia G Vigevano Federico F Mazzone Luigi L Valeriani Massimiliano M Moavero Romina R

Journal of clinical medicine 20210814 16

There is a high comorbidity between migraine and sleep disorders (SD), with a mutual dependence between sleep and headache. This study aimed to analyze the relationship between headache features (migraine frequency and severity, migraine equivalents, use and efficacy of treatments) and sleep in pediatric migraine. Parents of children and adolescents with migraine completed the Children's Sleep Habits Questionnaire (CSHQ) and the Epworth Sleepiness Scale for Children and Adolescents (ESS-CHAD) an ...[more]

PMID: 34441871

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Project description:Studies have shown a higher risk of depressive disorders in children with sleep apnea than in those without sleep apnea. However, the association between sleep apnea and subsequent depressive disorders in the pediatric population remains undetermined. Thus, this study investigated the risk of depressive disorders among pediatric patients with sleep apnea.In this study, the population-based National Health Insurance Research Database of Taiwan was used to identify patients who had first been diagnosed with sleep apnea between 1999 and 2013. Patients with sleep apnea who were younger than 18 years were included in the sleep apnea group. Controls (those without sleep apnea) were matched to patients with sleep apnea at a 1:10 ratio by age, sex, and index year. Patients who had baseline or inherited depressive disorders before the enrollment date were excluded. The two groups were followed up until December 31, 2013. The primary endpoint was the occurrence of one or more depressive disorders.At the end of this study, 6,237 children had been enrolled, comprising 567 children with sleep apnea and 5,670 children without sleep apnea. During the mean follow-up period of 5.87 years, a total of 77 children (1.23%) developed one or more depressive disorders; 14 (2.46%) from the sleep apnea group and 63 (1.11%) from the control group. Kaplan-Meier analysis showed that children with sleep apnea had a significantly higher risk of depressive disorders (log-rank test, p = 0.002). After adjusting for covariates, the risk of subsequent depressive disorders among children with sleep apnea was still significantly higher (hazard ratio [HR] = 2.25; 95% confidence interval [CI] = 1.25-4.05; p = 0.006). Moreover, boys with sleep apnea had a significantly higher risk than those without sleep apnea (adjusted HR = 3.77; 95% CI, 1.82-7.54; p < 0.001). Furthermore, in sleep apnea group, children older than 12 years of age had more risk to depression (hazard ratio (HR) = 7.1833, 95% confidence interval (CI), 2.3734 to 21.7411; p = 0.0004).This study found a significantly higher subsequent risk of depressive disorders in children with sleep apnea, particularly boys and those older than 12 years of age. The study findings strongly suggest that clinicians should provide psychological evaluation and supportive care for children with sleep apnea, in addition to medical treatment.

Project description:ObjectivesWe examined the cross-sectional association of sleep apnea and indices of sleep quality with both episodic migraine (EM) and chronic migraine (CM).BackgroundSleep apnea and abnormal patterns of sleep, such as insomnia, were associated with migraine onset, severity, and progression in previous research.MethodsThe Chronic Migraine Epidemiology & Outcomes Study, a longitudinal study, used a series of web-based surveys to assess migraine symptoms, burden, and patterns of health care utilization. Quota sampling was used from September 2012 to November 2013 to generate a representative sample of the US population. Persons who screened positive for sleep apnea on the Berlin Questionnaire are said to be at "high risk" for sleep apnea. Respondents indicated if they believed that they had sleep apnea, if a physician had diagnosed it, and if and how they were treated. Other aspects of sleep quality were assessed using the Medical Outcomes Study (MOS) Sleep Measures.ResultsOf 12,810 eligible respondents with migraine and data on sleep, 11,699 with EM (91.3%) and 1111 with CM (8.7%) provided valid data for this analyses. According to the Berlin Questionnaire, 4739/12,810 (37.0%) were at "high risk" for sleep apnea, particularly persons with CM vs EM (575/1111 [51.8%] vs 4164/11,699 [35.6%]), men vs women (1431/3220 [44.4%] vs 3308/9590 [34.5%]), people with higher body mass index, and older people (all P < .001). Among respondents to the MOS Sleep Measures, persons with CM were more likely to report poor sleep quality than those with EM, including sleep disturbance (mean [SD] values: 53.2 [26.9] vs 37.9 [24.3]), snoring (38.0 [33.9] vs 31.0 [32.1]), shortness of breath (34.9 [29.8] vs 15.3 [20.6]), somnolence (44.1 [23.4] vs 32.2 [21.2]), and less likely to report sleep adequacy (34.0 [24.2] vs 39.2 [22.1]).ConclusionsCompared with respondents with EM, a larger proportion of those with CM were at "high risk" for sleep apnea and reported poor sleep quality. This reflects an association between CM vs EM and sleep apnea and poor sleep quality; the potential relationships are discussed.

Project description:BackgroundSleep disorders, such as obstructive sleep apnea (OSA), comorbid insomnia and sleep apnea (COMISA), and insomnia are common and can have serious health consequences. However, accurately diagnosing these conditions can be challenging as a result of the underrecognition of these diseases, the time-intensive nature of sleep monitoring necessary for a proper diagnosis, and patients' hesitancy to undergo demanding and costly overnight polysomnography tests.ObjectiveWe aim to develop a machine learning algorithm that can accurately predict the risk of OSA, COMISA, and insomnia with a simple set of questions, without the need for a polysomnography test.MethodsWe applied extreme gradient boosting to the data from 2 medical centers (n=4257 from Samsung Medical Center and n=365 from Ewha Womans University Medical Center Seoul Hospital). Features were selected based on feature importance calculated by the Shapley additive explanations (SHAP) method. We applied extreme gradient boosting using selected features to develop a simple questionnaire predicting sleep disorders (SLEEPS). The accuracy of the algorithm was evaluated using the area under the receiver operating characteristics curve.ResultsIn total, 9 features were selected to construct SLEEPS. SLEEPS showed high accuracy, with an area under the receiver operating characteristics curve of greater than 0.897 for all 3 sleep disorders, and consistent performance across both sets of data. We found that the distinction between COMISA and OSA was critical for accurate prediction. A publicly accessible website was created based on the algorithm that provides predictions for the risk of the 3 sleep disorders and shows how the risk changes with changes in weight or age.ConclusionsSLEEPS has the potential to improve the diagnosis and treatment of sleep disorders by providing more accessibility and convenience. The creation of a publicly accessible website based on the algorithm provides a user-friendly tool for assessing the risk of OSA, COMISA, and insomnia.

Dataset Information

Sleep Disorders in Pediatric Migraine: A Questionnaire-Based Study.

Publications

Sleep Disorders in Pediatric Migraine: A Questionnaire-Based Study.

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